Steady-state Quantum Thermodynamics with Synthetic Negative Temperatures

A bath with a negative temperature is a subject of intense debate in recent times. It raises fundamental questions not only on our understanding of negative temperature of a bath in connection with thermodynamics but also on the possibilities of constructing devices using such baths. In this work, we study steady-state quantum thermodynamics involving baths with negative temperatures. A bath with a negative temperature is created synthetically using two baths of positive temperatures and weakly coupling these with a qutrit system. These baths are then coupled to each other via a working system. At steady-state, the laws of thermodynamics are analyzed. We find that whenever the temperatures of these synthetic baths are identical, there is no heat flow, which reaffirms the zeroth law. There is always a spontaneous heat flow for different temperatures. In particular, heat flows from a bath with a negative temperature to a bath with a positive temperature which, in turn, implies that a bath with a negative temperature is `hotter' than a bath with a positive temperature. This warrants an amendment in the Kelvin-Planck statement of the second law, as suggested in earlier studies. In all these processes, the overall entropy production is positive, as required by the Clausius statement of the second law. We construct continuous heat engines operating between positive and negative temperature baths. These engines yield maximum possible heat-to-work conversion efficiency, that is, unity. We also study the thermodynamic nature of heat from a bath with a negative temperature and find that it is thermodynamic work but with negative entropy.Comment: 7+2 pages, comments welcom

Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study

Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant chemotherapy at a single institution were used to train (n=85) and tune (n=15) a convolutional neural network (CNN) to predict pCR. A multi-input CNN leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was identified to achieve optimal response prediction within the validation set (AUC=0.93). This model was then tested on two independent testing cohorts with pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and a 29 patient multicenter trial including data from 3 additional institutions (AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction model was found to exceed a multivariable model incorporating predictive clinical variables (AUC < .65 in testing cohorts) and a model of semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing cohorts). The results presented in this work across multiple sites suggest that with further validation deep learning could provide an effective and reliable tool to guide targeted therapy in breast cancer, thus reducing overtreatment among HER2+ patients.Comment: Braman and El Adoui contributed equally to this work. 33 pages, 3 figures in main tex

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests

Bioengineering Approach to Elucidate How Cells Sense and Respond to Diverse Physical Cues

Cell migration is essential for a variety of (patho)physiological processes, such as immune surveillance, tissue morpho-dynamics and homeostasis and cancer metastasis. In vivo, metastasizing cancer cells migrate through complex, confining tissue microenvironments and interact with a variety of physical forces which can alter their migratory phenotype. How cells sense such mechanical cues and exert forces in their three-dimensional (3D) surroundings are still not completely known. We herein integrate microfabrication, live-cell microscopy, super-resolution imaging, molecular biology and optogenetic tools along with mathematical and in vivo models to identify how specific physical forces affect the process of cell migration and metastasis. We decipher the mechano-sensing mechanisms and downstream pathways involved in each of the processes. We also develop a novel biomimetic platform to study cell migration in high throughput manner under independently tuned stiffness and degree of confinement. We innovate 3D traction force measurements (TFM) around spatially constrained single cells using our platform. Following a discussion of our current understanding of physical cues in cancer metastasis and confined cell migration platforms, we investigate the effect of shear stress during intravasation. Using microfluidic devices, we mimic the entry of cells into shear flow inside blood vessels and demonstrate that normal fibroblasts avoid shear stress, similar to what happens physiologically. Our data reveal a mechanism where shear stress upregulates RhoA activity by inducing calcium influx via the mechanosensitive transient receptor potential cation channel, subfamily M, member 7 (TRPM7). In distinct contrast, cancer cells derived from fibroblasts (fibrosarcoma), have lower shear sensitivity due to reduced expression of TRPM7. When fibrosarcoma cells are engineered to overexpress TRPM7-YFP, they display increased shear sensitivity and significantly reduced intravasation in vivo, compared to YFP control cells. Next, we evaluate the effect of physiologically relevant fluid viscosity on cell migration. The viscosity of the interstitial fluid is a critical physical parameter that varies under both physiological and pathological conditions, such as cancer. However, its impact on cell biology and the mechanism by which cells sense and respond to changes in extracellular viscosity are unknown. We demonstrate that elevated viscosity counterintuitively increases the motility of various cell types in confinement, on two-dimensional (2D) surfaces, and cell dissemination from 3D tumor spheroids. Increased mechanical loading imposed on the cell leading edge by elevated viscosity induces an Actin Related Protein 2/3 (Arp2/3) complex-dependent dense actin network, which enhances Na+/H+ exchanger 1 (NHE1) polarization via its actin-binding partner ezrin. NHE1 together with aquaporin 5 promote cell swelling which, in turn, activates transient receptor potential cation vanilloid 4 (TRPV4) and enhances calcium influx that leads to increased RhoA-dependent cell contractility. The coordinated action of actin remodeling/dynamics, NHE1-mediated swelling and RhoA-based contractility facilitates enhanced motility at elevated viscosities. Moreover, breast cancer cells exposed to elevated viscosity exhibit increased migration in zebrafish and lung metastasis in mice, indicating an ability to memorize prior mechanical stress. Cumulatively, extracellular viscosity is a physical cue that can regulate key (patho)physiological processes like metastasis. Finally, we apply our soft-lithography expertise and sulfo-N-hydroxysuccinimide (NHS) esters as crosslinkers to develop a novel polyacrylamide (PA) based microfluidic device with an array of microchannels which are surrounded by physiologically relevant compliant walls on four sides. The design enables us to subject cells to confinement with anisotropic stiffnesses on the apico-lateral versus basal surfaces. Ultimately by embedding nanobeads in the PA gel, we are able to perform spatiotemporally resolved 3D TFM around migrating single cells. Overall, this dissertation highlights the critical role of mechanical cues in regulating cell migration and how bioengineering approaches can be leveraged to tease apart such contributions in the metastatic cascade

Murine double minute 2, a potential p53-independent regulator of liver cancer metastasis

Hepatocellular carcinoma (HCC) has emerged as one of the most commonly diagnosed forms of human cancer; yet, the mechanisms underlying HCC progression remain unclear. Unlike other cancers, systematic chemotherapy is not effective for HCC patients, while surgical resection and liver transplantation are the most viable treatment options. Thus, identifying factors or pathways that suppress HCC progression would be crucial for advancing treatment strategies for HCC. The murine double minute 2 (MDM2)-p53 pathway is impaired in most of the cancer types, including HCC, and MDM2 is overexpressed in approximately 30% of HCC. Overexpression of MDM2 is reported to be well correlated with metastasis, drug resistance, and poor prognosis of multiple cancer types, including HCC. Importantly, these correlations are observed even when p53 is mutated. Indeed, p53-independent functions of overexpressed MDM2 in cancer progression have been suitably demonstrated. In this review article, we summarize potential effectors of MDM2 that promote or suppress cancer metastasis and specifically discuss the p53-independent roles of MDM2 in liver cancer metastasis from clinical as well as biological perspectives